In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
print('imported successfully')
In [2]:
df=pd.read_csv('Data_Collisions.csv')
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df.head()
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In [4]:
import seaborn as sns
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weather = df['WEATHER']
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weather
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In [7]:
weather.head
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In [8]:
plt.figure(figsize=(15,15))
ax = sns.countplot(x="WEATHER", data=df)
More accidents have been reported in clear weather followed by overcast and rainy weather
In [9]:
plt.figure(figsize=(15,15))
ax = sns.countplot(x="ROADCOND", data=df)
More accidents have been reported in dry road conditions followed by wet and unknown circumstances
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plt.figure(figsize=(15,15))
ax = sns.countplot(x="LIGHTCOND", data=df)
More accidents have been reported in daylight followed by street light and unknown circumstances
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plt.figure(figsize=(15,25))
ax = sns.countplot(x="LOCATION", data=df)
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df['LOCATION'].count()
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In [13]:
df.groupby('LOCATION').count()
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In [14]:
Location = df['LOCATION'].value_counts()
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Location
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df.head()
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In [17]:
df.info
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In [18]:
Location.describe
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df2 = pd.DataFrame(data=Location)
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df2
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In [22]:
Location.describe
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In [23]:
#Creating a dictionary for LIGHTCOND
light = {'Daylight': 1,'Dark - Street Lights On': 2,'Dusk': 3, 'Dark - No Street Lights': 4 }
light
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In [24]:
df['LIGHTCOND'] = df['LIGHTCOND'].map(light)
df['LIGHTCOND']
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In [25]:
z = df['LIGHTCOND']
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z
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In [30]:
import plotly.graph_objects as go
import seaborn as sns
#from ggplot import *
print('imported successfully')
In [31]:
#creating the heatmap
fig = go.Figure(data=go.Heatmap(
x = df['WEATHER'],
y = df['LOCATION'],
z = z,
type = 'heatmap',
colorscale = 'Viridis', hoverongaps = False))
In [32]:
fig.show()